PET scanner quality check control is performed on a daily basis. In Siemens scanners, for example, data is acquired from a uniform cylinder and the estimation of a crystal efficiency (CE) normalization component is carried out. Besides producing normalization array, the CEs analysis can also determine whether additional calibration should be performed.
PET is an imaging method that is used in nuclear medicine and radiation therapy. During PET, a positron is emitted inside the body of a patient subject being examined due to radioactive decay. The relevant radioactive decay may be induced, for example, by injection or inhalation of a radioactively marked radiopharmaceutical, such as a tracer. Disease information may be determined based on the spatial distribution of the tracer. After a short distance, the positron enters into interaction with an electron. The interaction destroys both particles. The destruction creates a pair of gamma quanta. The quanta are at an angle of 180° from one another. The gamma quanta penetrate the patient body and after exiting the body are recorded by two opposed detectors. A PET scanner for imaging includes a plurality of gamma radiation detectors, which surround the patient to be examined.
One of the physics ingredients of PET is scanner detector efficiencies, which are typically assumed to be known before image reconstruction. A component-based method is commonly used to model the normalization factors, which are the inverse of efficiencies for each Line-of-Response (LOR). A majority of components, such as geometric and crystal interference, are estimated once for a particular scanner type. Contrary to this, the crystal efficiency (CE) normalization component is estimated on a regular basis.
A number of methods to estimate CE normalization component have been developed. For example, the method from Defrise [M. Defrise, et al., “A Normalization Technique for 3D PET Data”, Phys. Med. Biol., 36, 939-952, 1991.] is an exact analytical method that typically uses only part of the available data. The fan sum method, which uses all available data, is not exact and might lead to bias in a very uneven efficiency distribution. CE normalization component can be estimated by the Maximum Likelihood (ML) approach. This approach has the advantage of versatility, where all available data are easily accommodated.
PET scanner calibration is a routine procedure that is usually performed daily in order to provide accurate results when a patient is subjected to a scan. In some scanners, for example, data are acquired for about 20 to 30 minutes each day using a phantom that is a 20 cm diameter uniform cylinder. By assuming a known object (e.g., the 20 cm diameter uniform cylinder) an estimation of a scintillation CE normalization component is conducted, since the rest of the normalization components are fixed for a given scanner type.
Normalization factors are corrections that compensate for non-uniformity of PET detector pair efficiencies. A component-based method is used to improve accuracy of the normalization factors. Most components, such as geometric and crystal interference components, can be estimated in advance for a particular scanner type. This is contrary to the scintillation CE normalization component, which is estimated on a regular basis. Besides producing a normalization array, the crystal efficiency values are used in daily Quality Control (QC) procedures. In this procedure, particular crystal block sensitivities are checked against the average scintillation detector crystal block sensitivity. A significant deviation from the average crystal block sensitivity will signal for replacement or monitoring of the particular crystal block. Potentially, data originating from this particular crystal block maybe excluded during list mode data histogramming and reconstruction. A scintillation detector crystal block is an array of crystals and consists of many crystals, each referred to as a pixel.
The use of frequent phantom scans is not ideal. Self-normalization (estimation of the normalization array from unknown object data) has been suggested as an alternative, but in non-TOF, an acceptable solution can be achieved only with the use of significant a priori knowledge. The TOF self-normalization problem was proposed in, where crystal efficiencies were estimated with the help of detector singles measurements. However, such measurements are not available on all scanners. Similar information can be extracted from random events data on Siemens scanners. However, this singles estimation is of a low count nature and is used for random variance reduction. Singles modeling is equivalent to a non-collimated single-photon emission computed tomography (SPECT) problem formulation. This requires the development of an additional reconstruction model. Finally, singles efficiencies may not correlate well with efficiencies for coincidence events in PET scanning.
Calibration is therefore complicated, since a phantom radioactive sources have to be set up in the treatment chamber and then removed. Such process requires manual intervention, involves cost, and can suffer from errors.
Inventor has previously provided a method for calibrating a PET detector by reconstructing the estimation of a scintillation CE normalization component simultaneously along with the reconstruction of the positron annihilation activity from TOF patient data. The method is described in United States patent publication No. 2015/0297168, the contents of which are incorporated herein by reference. The method allows monitoring of PET scanner performance as step-and-shoot (S&S) patient scans are performed, eliminating the need for the separate quality check scans.
In continuous bed motion (CBM) acquisition, however, the continuous motion of the patient may result in inaccuracies in the PET model for the normalization coefficient. Therefore, there is a need for improved methods for simultaneous monitoring of PET scanner's CEs normalization component in CBM acquisition mode.